Abstract
Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.
Highlights
In addition to careful analyses of clinical symptoms, numerous diagnostic methods have been used to diagnose cancer [1]
With the increasing amount of available data from visual images over recent years, numerous diagnostic techniques for cancer have been developed through machine learning methods such as convolutional neural networks (CNNs) [2,3]
In order to identify such correlations, we developed a simple deep neural networks (DNNs) model using a data set of neuroblastoma patients including gene expression data and clinical data (i.e., International Neuroblastoma Staging System (INSS) stages)
Summary
In addition to careful analyses of clinical symptoms, numerous diagnostic methods have been used to diagnose cancer [1]. With the increasing amount of available data from visual images over recent years, numerous diagnostic techniques for cancer have been developed through machine learning methods such as convolutional neural networks (CNNs) [2,3]. Categorical classification models that predict cancer stages or types of cancer have been constructed on the basis of image data [4,5]. In addition to image data, basic classifications, such as a diagnosis of cancerous versus healthy tissue, can be performed through gene expression data, and models have been developed using traditional machine learning methods. Genomic data can be used as a proxy for the early diagnosis of cancer, meaning that models based on gene expression
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